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Nonparametric test for connectivity detection in multivariate autoregressive networks and application to multiunit activity data

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dc.contributor.author Gilson, Matthieu
dc.contributor.author Tauste Campo, Adrià
dc.contributor.author Chen, X.
dc.contributor.author Thiele, Alexander
dc.contributor.author Deco, Gustavo
dc.date.accessioned 2018-04-24T09:51:12Z
dc.date.available 2018-04-24T09:51:12Z
dc.date.issued 2017
dc.identifier.citation Gilson M, Tauste Campo A, Chen X, Thiele A, Deco G. Nonparametric test for connectivity detection in multivariate autoregressive networks and application to multiunit activity data. Netw Neurosci. 2017;1(4): 357-80. DOI: 10.1162/NETN_a_00019
dc.identifier.issn 2472-1751
dc.identifier.uri http://hdl.handle.net/10230/34429
dc.description.abstract Directed connectivity inference has become a cornerstone in neuroscience to analyze multivariate data from neuroimaging and electrophysiological techniques. Here we propose a nonparametric significance method to test the nonzero values of multivariate autoregressive model to infer interactions in recurrent networks. We use random permutations or circular shifts of the original time series to generate the null-hypothesis distributions. The underlying network model is the same as used in multivariate Granger causality, but our test relies on the autoregressive coefficients instead of error residuals. By means of numerical simulation over multiple network configurations, we show that this method achieves a good control of false positives (type 1 error) and detects existing pairwise connections more accurately than using the standard parametric test for the ratio of error residuals. In practice, our method aims to detect temporal interactions in real neuronal networks with nodes possibly exhibiting redundant activity. As a proof of concept, we apply our method to multiunit activity (MUA) recorded from Utah electrode arrays in a monkey and examine detected interactions between 25 channels. We show that during stimulus presentation our method detects a large number of interactions that cannot be solely explained by the increase in the MUA level.
dc.description.sponsorship MG acknowledges funding from the Marie Sklodowska-Curie Action (grant H2020-MSCA656547). MG and GD were supported by the Human Brain Project (grant FP7-FET-ICT-604102 and H2020-720270 HBP SGA1). GD and ATC were supported by the European Research Council Advanced Grant DYSTRUCTURE (Grant 295129). AT was supported by the UK Medical Research Council (grant MRC G0700976).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher MIT Press
dc.relation.ispartof Network Neuroscience. 2017;1(4): 357-80.
dc.rights © 2017 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. Publisher version at http://mitpress.mit.edu
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title Nonparametric test for connectivity detection in multivariate autoregressive networks and application to multiunit activity data
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1162/NETN_a_00019
dc.subject.keyword Network connectivity detection
dc.subject.keyword Nonparametric significance method
dc.subject.keyword Multivariate autoregressive process
dc.subject.keyword Granger causality
dc.subject.keyword Multiunit activity
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/656547
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/604102
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/720270
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/295129
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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